자료유형 | 학위논문 |
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서명/저자사항 | Passive Radar Detection and Imaging Using Low-rank Matrix Recovery. |
개인저자 | Mason, Eric. |
단체저자명 | Rensselaer Polytechnic Institute. Electrical Engineering. |
발행사항 | [S.l.]: Rensselaer Polytechnic Institute., 2017. |
발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2017. |
형태사항 | 202 p. |
기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
ISBN | 9780438206212 |
학위논문주기 | Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2017. |
일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Birsen Yazici. |
요약 | The objective of this thesis is to develop passive radar imaging methods in an optimization framework that utilize prior information. Passive radar relies on transmitters of opportunity such as commercial television, radio, and cell phone base s |
요약 | First, this thesis presents a non-linear optimization based reconstruction method for passive radar that overcomes the drawbacks of currently used Fourier based methods, such as passive coherent localization (PCL) and time difference of arrival |
요약 | Next, we study the performance of the convex LRMR based approach. We show that at sufficiently high center frequencies and commonly used imaging configurations the convex LRMR method recovers the scene reflectivity exactly. Furthermore, we deriv |
요약 | We then use non-convex optimization methods to reduce computational complexity and enforce the rank-one structure directly. We derive a descent algorithm using the majorization-minimization framework and prove convergence to an optimal solution |
요약 | Then we study the structure of orthogonal frequency division multiplexed (OFDM) waveforms used by common television and cellular illuminators of opportunity. Using this waveform model, we pose joint estimation as maximum a posteriori (MAP) estim |
일반주제명 | Electrical engineering. Applied mathematics. |
언어 | 영어 |
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